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SimStudent: A computational model of learning for Intelligent Authoring and beyond

SimStudent: A computational model of learning for Intelligent Authoring and beyond. Noboru Matsuda Human-Computer Interaction Institute C arnegie M ellon U niversity. CTAT: Cognitive Tutor Authoring Tools. Example-Tracing Tutor Zero programming

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SimStudent: A computational model of learning for Intelligent Authoring and beyond

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  1. SimStudent:A computational model of learning for Intelligent Authoring and beyond Noboru Matsuda Human-Computer Interaction Institute Carnegie Mellon University

  2. CTAT: Cognitive Tutor Authoring Tools Example-Tracing Tutor Zero programming A cognitive model specific to a particular problem Limited generalization by editing a behavior graph Model-TracingTutor Requires a cognitive model Cognitive task analysis is hard Writing production rules is even more challenging Performing a task is much easier…

  3. Next Generation Authoring Build a tutor GUI Teaching a solution SimSt. learning Production Rules Rule simplify-LHS: IF is-equation( Eq ), is-lhs( Eq, Lhs ), polynomial( Lhs ), all-var-terms( Lhs ) Then simplify( Lhs, S-lhs ), enter( S-lhs ) Rule simplify-LHS: IF is-equation( Eq ), is-lhs( Eq, Lhs ), polynomial( Lhs ), all-var-terms( Lhs ) Then simplify( Lhs, S-lhs ), enter( S-lhs ) Rule simplify-LHS: IF is-equation( Eq ), is-lhs( Eq, Lhs ), polynomial( Lhs ), all-var-terms( Lhs ) Then simplify( Lhs, S-lhs ), enter( S-lhs )

  4. SimStudent Machine learning agent Learns problem-solving steps by … Observes model solutions / solving problems, and … Outputs a set of production rules Fundamental technology Programming by Demonstration Inductive Logic Programming Lau & Weld (1998). Blessing (1997).

  5. Authoring Strategies • Authoring by demonstration • Demonstrate whole solutions • Learning from worked-out examples • Demonstrated solutions as positive examples • Authoring by tutoring • Interactively tutor with immediate feedback and hint • Learning by tutored problem-solving • Learning by generalizing hint with taking feedback into account

  6. Demo! Authoring by Tutoring

  7. Learning Production Rules in 3 parts: What-When-How If such and such constraints hold When among this and that GUI elements What Then do actions with the GUI elements How

  8. Production Rule in JESS GUI elements Constraints Actions

  9. Background Knowledge Domain concepts to “explain” demonstrations Operators Feature predicates External Jess function written in Java (defrule multi-lhs … ?var22140 <- (column (cells ? ? ?var22143 ? ? ? ? ?)) ?var22143 <- (cell (value ?val0&~nil)) (test (fraction-term ?val0 )) => (bind ?val2 (denominator ?val0)) (bind ?input (mul-term-by ?val0 ?val2)) … )

  10. Example： Algebra domain • 16 Feature predicates & 28 operators

  11. Learning Results Authoring by tutoring Betterthan Authoring by demonstration % Correct rule firings (10 test tasks) # of training tasks

  12. Authoring Time Authoring by Tutoring • Authoring by tutoring took 86 minutes • Authoring by demonstration took 238 minutes • A 2.8x speed-up! Authoring by Demonstration

  13. Example: Stoichiometry Tutor

  14. SimStudent Projects • Intelligent Authoring • Building a Cognitive Tutor as a CTAT Plug-in • Student Modeling and Simulation • Controlled educational studies • Error formation study • Prerequisite conceptual knowledge study • Teachable Peer Learner • Learning by teaching

  15. SimStudent Projects • Intelligent Authoring • Building a Cognitive Tutor as a CTAT Plug-in • Student Modeling and Simulation • Controlled educational studies • Error formation study • Prerequisite conceptual knowledge study • Teachable Peer Learner • Learning by teaching

  16. Model of Incorrect Learning • Identify errors students commonly make • Weaken SimStudent’s background knowledge • Let SimStudent make an induction error

  17. Weak Prior Knowledge Hypothesis • Multiple ways to make sense of examples strong prior knowledge Get a coefficient and divide Get a denominator and multiply “multiply by x” 3x=5 “divide by 3” 4/x=5 “divide by 4” Get a number and divide weak prior knowledge

  18. Target Common Errors

  19. Results: Learning Rate Steps Score = 0 (if there is no rule applicable) # correct rule applications / # all rule applications Step Score # training problems

  20. SimStudent Projects • Intelligent Authoring • Building a Cognitive Tutor as a CTAT Plug-in • Student Modeling and Simulation • Controlled educational studies • Error formation study • Prerequisite conceptual knowledge study • Teachable Peer Learner • Learning by teaching

  21. Learning by Teaching SimStudent

  22. Demo! Learning by Teaching

  23. Learn more about SimStudents • Project Web • www.SimStudent.org • Download & Tutorial • http://ctat.pact.cs.cmu.edu (linked from project web) • Contact us! • mazda@cs.cmu.edu

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